Yazar "Sharif, Md. Haidar" seçeneğine göre listele

Listeleniyor 1 - 3 / 3
  • Küçük Resim Yok
    Yayın
    Controlling electrical appliance by thinking in mind
    (Maltepe Üniversitesi, 2019) Sharif, Md. Haidar; Uyaver, Şahin
    Brain Computer Interface (BCI) technologies open up a world of possibilities. They use signals recorded from the brain (e.g., EEG: Electroencephalography) to apply miscellaneous controls and communications without using any external devices or muscle intervention. Their applications include but not limited to: (i) Brain to device control, (ii) Device to brain control, (iii) Brain to Internet communications with an infinite amount of information storage and retrieval, (iv) Mind to mind communication, (v) Memories and feelings transformation, and (vi) Brain to brain control. However, BCI technologies are still in its emerging stages. This paper demonstrates a brain to device control application for controlling electrical appliances by deeming mind thinking signal of the EEG.
  • Küçük Resim Yok
    Yayın
    Facial expression recognition using deep learning
    (Maltepe Üniversitesi, 2021) Shehu, Harisu Abdullahi; Sharif, Md. Haidar; Uyaver, Sahin
    t. Facial expression recognition has become an increasingly important area of research in recent years. Neural networkbased methods have made amazing progress in performing recognition-based tasks, winning competitions set up by various data science communities, and achieving high performance on many datasets. Miscellaneous regularization methods have been utilized by various researchers to help combat over-fitting, to reduce training time, and to generalize their models. In this paper, by applying the Haar Cascade classifier to crop faces and focus on the region of interest, we hypothesize that we would attain a fast convergence without using the whole image to analyze facial expressions. We also apply label smoothing and analyze its effect on the databases of CK+, KDEF, and RAF. The ResNet model has been employed as an example of a neural network model. Label smoothing has demonstrated an improvement of the recognition accuracy up to 0.5% considering CK+ and the KDEF databases. While the application of Haar Cascade has shown to decrease the achieved accuracy on KDEF and RAF databases with a small margin, fast convergence of the model has been observed.
  • Küçük Resim Yok
    Yayın
    Sentiment analysis of turkish twitter data
    (Maltepe Üniversitesi, 2019) Shehu, Harisu Abdullahi; Tokat, Sezai; Sharif, Md. Haidar; Uyaver, Şahin
    In this paper, we present a mechanism to predict the sentiment on Turkish tweets by adopting two methods based on polarity lexicon (PL) and artificial intelligence (AI). The method of PL introduces a dictionary of words and matches the words to those in the harvested tweets. The tweets are then classified to be either positive, negative, or neutral based on the result found after matching them to the words in the dictionary. The method of AI uses support vector machine (SVM) and random forest (RF) classifiers to classify the tweets as either positive, negative or neutral. Experimental results show that SVM performs better on stemmed data by achieving an accuracy of 76%, whereas RF performs better on raw data with an accuracy of 88%. The performance of PL method increases continuously from 45% to 57% as data are being modified from a raw data to a stemmed data.